PK NS9ăŃÓËŘ┼ Ř┼ refs.MYD■Í ■? Hopkins, W G 20048 How to interpret changes in an athletic performance test 1-7 Sportscience 8ő Bayes, correlation, error of the estimate, error of measurement, limits of agreement, reliability, time to exhaustion, time trial, validityb When monitoring progression of an athlete with performance or other fitness tests, it is important to take into account the magnitude of the smallest worth-while change in performance and the uncertainty or noise in the test result. For elite athletes competing in sports as individuals, the smallest worthwhile change in performance is about half the typical variation in an athlete's per-formance from competition to competition, or ~0.5-1% when expressed as a change in power output, depending on the sport. In team sports, where there is no direct relationship between team and test performance, an appropriate default for the smallest change in test performance is one-fifth of the between-athlete standard deviation (a standardized or Cohen effect size of 0.20). Noise in a test result is best expressed as the typical or standard error of measurement derived from a reliability study. The noise in most perform-ance tests is greater than the smallest worthwhile difference, so assessments of changes in performance can be problematic. An exact but somewhat impractical solution is to present chances that the true change is beneficial, trivial, and harmful. A simpler approach is to apply systematic rules to decide whether the true change is beneficial, trivial, harmful, or unclear. Unrealistically large changes can also be partially discounted when tests are noisy.( http://sportsci.org/jour/04/wghtests.htmâ Sport and Recreation, Faculty of Health, Auckland University of Technology, Auckland 1020, New Zealand. Email: will=AT=clear.net.nzĐ■Í ■? Batterham, A M
Hopkins, W G 2005- Making meaningful inferences about magnitudes 6-13 Sportscience 9B clinical significance, confidence limits, statistical significance8 A study of a sample provides only an estimate of the true (population) value of an outcome statistic. A report of the study therefore usually includes an infer-ence about the true value. Traditionally, a researcher makes an inference by declaring the value of the statistic statistically significant or non-significant on the basis of a p value derived from a null hypothesis test. This approach is confusing and can be misleading, depending on the magnitude of the statistic, error of measurement, and sample size. We use a more intuitive and practical approach based directly on uncertainty in the true value of the statistic. First we express the uncertainty as confidence limits, which define the likely range of the true value. We then deal with the real-world relevance of this uncertainty by taking into account values of the statistic that are substantial in some posi-tive and negative sense, such as beneficial and harmful. If the likely range overlaps substantially positive and negative values, we infer that the outcome is unclear; otherwise, we infer that the true value has the magnitude of the observed value: substantially positive, trivial, or substantially negative. We refine this crude inference by stating qualitatively the likelihood that the true value will have the observed magnitude (e.g., very likely beneficial). Quantita-tive or qualitative probabilities that the true value has the other two magnitudes or more finely graded magnitudes (such as trivial, small, moderate, and large) can also be estimated to guide a decision about the utility of the outcome.&